vowpal_wabbit training data - svm

I am trying to understand vowpal_wabbit data structure for the training and test data but cannot seem to understand them.
I have some training data like.
Feature 1: 0
Feature 2: 1
Feature 3: 10
Feature 4: 5
Class label : A
Feature 1: 0
Feature 2: 2
Feature 3: 30
Feature 4: 8
Class label : C
Feature 1: 2
Feature 2: 10
Feature 3: 9
Feature 4: 7
Class label : B
I have explored some training data examples based on this website.
http://hunch.net/~vw/validate.html
My Validation data
1 | haha:1 hehe:2 hoho:3
1 | haha:2 hehe:2 hoho:3
3 | haha:3 hehe:2 hoho:3
1 | haha:4 hehe:2 hoho:3
2 | haha:5 hehe:2 hoho:3
However, I do not understand why it claims that I have 4 and 5 features respectively.
Validation:
Validation Feedback
Total of 5 examples pasted.
(example #1) Example “1 | haha:1 hehe:2 hoho:3”.
(example #1) Found “[label] |…” prefix format.
(example #1) Example label / response / class is “1”.
(example #1) Example has default “1.0” importance weight.
(example #1) Example has default “0” base.
(example #1, namespace #1) Using default namespace.
(example #1, namespace #1) Found 3 feature(s).
(example #1, namespace #1, feature #1) Label “haha”.
(example #1, namespace #1, feature #1) Value “1”.
(example #1, namespace #1, feature #2) Label “hehe”.
(example #1, namespace #1, feature #2) Value “2”.
(example #1, namespace #1, feature #3) Label “hoho”.
(example #1, namespace #1, feature #3) Value “3”.
(example #2) Example “1 | haha:2 hehe:2 hoho:3 ”.
(example #2) Found “[label] |…” prefix format.
(example #2) Example label / response / class is “1”.
(example #2) Example has default “1.0” importance weight.
(example #2) Example has default “0” base.
(example #2, namespace #1) Using default namespace.
(example #2, namespace #1) Found 4 feature(s).
(example #2, namespace #1, feature #1) Label “haha”.
(example #2, namespace #1, feature #1) Value “2”.
(example #2, namespace #1, feature #2) Label “hehe”.
(example #2, namespace #1, feature #2) Value “2”.
(example #2, namespace #1, feature #3) Label “hoho”.
(example #2, namespace #1, feature #3) Value “3”.
(example #2, namespace #1, feature #4) Label “”.
(example #2, namespace #1, feature #4) Using default value of “1” for feature.
(example #3) Example “3 | haha:3 hehe:2 hoho:3 ”.
(example #3) Found “[label] |…” prefix format.
(example #3) Example label / response / class is “3”.
(example #3) Example has default “1.0” importance weight.
(example #3) Example has default “0” base.
(example #3, namespace #1) Using default namespace.
(example #3, namespace #1) Found 4 feature(s).
(example #3, namespace #1, feature #1) Label “haha”.
(example #3, namespace #1, feature #1) Value “3”.
(example #3, namespace #1, feature #2) Label “hehe”.
(example #3, namespace #1, feature #2) Value “2”.
(example #3, namespace #1, feature #3) Label “hoho”.
(example #3, namespace #1, feature #3) Value “3”.
(example #3, namespace #1, feature #4) Label “”.
(example #3, namespace #1, feature #4) Using default value of “1” for feature.
(example #4) Example “1 | haha:4 hehe:2 hoho:3 ”.
(example #4) Found “[label] |…” prefix format.
(example #4) Example label / response / class is “1”.
(example #4) Example has default “1.0” importance weight.
(example #4) Example has default “0” base.
(example #4, namespace #1) Using default namespace.
(example #4, namespace #1) Found 4 feature(s).
(example #4, namespace #1, feature #1) Label “haha”.
(example #4, namespace #1, feature #1) Value “4”.
(example #4, namespace #1, feature #2) Label “hehe”.
(example #4, namespace #1, feature #2) Value “2”.
(example #4, namespace #1, feature #3) Label “hoho”.
(example #4, namespace #1, feature #3) Value “3”.
(example #4, namespace #1, feature #4) Label “”.
(example #4, namespace #1, feature #4) Using default value of “1” for feature.
(example #5) Example “2 | haha:5 hehe:2 hoho:3 ”.
(example #5) Found “[label] |…” prefix format.
(example #5) Example label / response / class is “2”.
(example #5) Example has default “1.0” importance weight.
(example #5) Example has default “0” base.
(example #5, namespace #1) Using default namespace.
(example #5, namespace #1) Found 5 feature(s).
(example #5, namespace #1, feature #1) Label “haha”.
(example #5, namespace #1, feature #1) Value “5”.
(example #5, namespace #1, feature #2) Label “hehe”.
(example #5, namespace #1, feature #2) Value “2”.
(example #5, namespace #1, feature #3) Label “hoho”.
(example #5, namespace #1, feature #3) Value “3”.
(example #5, namespace #1, feature #4) Label “”.
(example #5, namespace #1, feature #4) Using default value of “1” for feature.
(example #5, namespace #1, feature #5) Label “”.
(example #5, namespace #1, feature #5) Using default value of “1” for feature.


why it claims that i have 4 and 5 features respectively
The extra space symbols at the end of lines are interpreted as extra features by http://hunch.net/~vw/validate.html. (Yes, the last line in your sample has two extra spaces.) Note that validate.html reports an empty name of the extra features:
(example #4, namespace #1, feature #4) Label “”.
Note that validate.html is implemented in JavaScript and is completely independent on the implementation of VW itself (which is in C). VW ignores the trailing spaces. You can test it with:
$ vw -P 1 < sample.data
...
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 1 1.0 1.0000 0.0000 4
0.522042 0.044084 2 2.0 1.0000 0.7900 4
1.838150 4.470366 3 3.0 3.0000 0.8857 4
1.488676 0.440255 4 4.0 1.0000 1.6635 4
1.270585 0.398217 5 5.0 2.0000 1.3690 4
So all five example are reported as having 4 features (see the last column).
Why four? There is an extra constant (intercept) feature automatically added. If you don't want it, you can use vw --noconstant.

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